At Cisco Dwell in San Diego, D.J. Sampath, Senior Vice President of Cisco’s AI Software program and Platform group, wowed the gang with a demo of AI Canvas. That’s a multi-data, multi-agent system, built-in with Cisco’s AI Assistant and powered by Cisco’s Deep Community Mannequin. In that demo, we may all see AI Canvas’s means to hurry troubleshooting, deliver siloed groups collectively, and allow automation throughout your entire stack.
AI Canvas gained’t be obtainable till October. Nonetheless, we needed to supply our CCIEs, CCDEs, and Cisco Licensed DevNet Specialists the chance to work with the Deep Community Mannequin as quickly as doable. So we’re making the mannequin obtainable to CCIEs and different consultants via an AI Studying Assistant obtainable in Cisco U.
We expect CCIEs (and shortly, different community engineers) will discover a wealth of ways in which the Deep Community Mannequin may help them study extra and develop into extra environment friendly. However we notice that agentic ops is model new, and that you simply is likely to be questioning how one can instantly begin experimenting with the Deep Community Mannequin. So I assumed I’d supply some pattern use circumstances that will help you get began.
Tailor-made eventualities and coaching paths
As a CCIE, you’ve bought years—generally many years—of expertise in networking, and also you’re absolutely in control in your group’s IT infrastructure. However what about your staff members, particularly extra junior community engineers? The Deep Community Mannequin AI Assistant can be utilized to construct tailor-made eventualities and coaching concepts so that everybody in your staff can study the talents wanted for the community you at present have, in addition to any new applied sciences your group plans to roll out.
The Deep Community Mannequin understands a variety of networking applied sciences, however it’s skilled explicitly on a depth and breadth of Cisco-specific materials. It’s additionally skilled on the supplies and coursework obtainable in Cisco U. You would possibly strive a immediate equivalent to this one:
- I’m the tech lead for a small staff of community engineers. I have to rapidly get them in control on the networking know-how we use in our surroundings, together with BGP, MPLS, and OSPF. Might you construct me a customized examine plan?
Once I requested this query of the Deep Community Mannequin AI Assistant, I bought a really good syllabus in define type, with hyperlinks to programs in Cisco U.
Right here’s a pattern:
Design validation and optimization
Cisco Validated Designs (CVDs) are primarily blueprints, and IT professionals are accustomed to working via them. However generally you want extra steering. The Deep Community Mannequin AI Assistant may help make CVDs extra navigable. It may possibly entry different sources to assist flesh out CVDs and supply solutions for bettering or optimizing designs.
It may possibly additionally summarize the CVD, supplying you with a high-level overview earlier than studying the entire thing. You possibly can ask it questions equivalent to:
- Contemplating the CVD for FlexPod, present a getting-started doc that I can use to configure my preliminary UCS supervisor.
- I’m starting to implement the CVD for FlexPod. Might you give me a high-level overview of what I’ll be doing and the items I’ll be working with?
The Deep Community Mannequin AI Assistant may help validate an present design with respect to a CVD and supply solutions for bettering or optimizing designs.
- What sort of storage know-how ought to I take into account for booting my blades in a UCS B chassis?
In the event you’re having points with a CVD, you’ll be able to ask the Deep Community Mannequin AI Assistant the place you must begin trying.
Automation assistant
The Deep Community Mannequin AI Assistant may also assist with automation. You might ask it questions equivalent to:
- I’m an skilled in community structure and want some assist automating our department SD-WAN deployment. What can be a well-supported, easy-to-learn software that will assist me help this? My staff doesn’t have a substantial amount of coding expertise. Might you present examples and hyperlinks to related documentation and coaching?
Troubleshooting
The Deep Community Mannequin AI Assistant may help analyze community diagnostics, equivalent to syslog messages and debug output, and study drawback signs to supply perception that is likely to be missed by human eyes. Though generative AI continues to be a younger know-how that may make errors, expert-level IT professionals are well-equipped to judge the output for accuracy and detect hallucinations.
For instance, the Deep Community Mannequin AI Assistant may assist interpret a syslog message. You might merely enter the message into the assistant and say you want recommendation or a spot to begin. As a result of it’s skilled on Cisco’s syslog codecs, it may give steering and cross-reference different information.
In the event you’re working with a number of information sources, the evaluation turns into extra advanced. With the Deep Community Mannequin AI Assistant, you’ll be able to describe the symptom you see after which ask, “What ought to I search for?” (After all, you must at all times watch out about pasting uncooked output into AI.) On this approach, you should use the assistant to information you to the purpose the place you’re comfy taking on.
Plenty of debugging is actually several types of diagnostic information and looking for the needle in a haystack that can assist what to do subsequent. The Deep Community Mannequin AI Assistant may help with that course of. For instance, if it’s essential to troubleshoot routing adjacencies, you’ll seemingly want to assemble information from a number of units and correlate the information to establish a root trigger.
You are a community troubleshooting assistant. Assist me diagnose why my OSPFv3 session shouldn’t be establishing with one neighbor. That is the output from ‘present ospfv3 neighbor’:
OSPFv3 1 address-family ipv6 (router-id 192.0.2.1)
Neighbor ID Pri State Useless Time Interface ID Interface 192.0.2.2 128 EXCHANGE/BDR 00:00:38 13 Vlan300 192.0.2.6 128 FULL/DR 00:00:37 5 Vlan300 And that is the related config from Vlan300: ipv6 handle FE80::300:241 link-local ipv6 handle 2001:DB8::241/64 ipv6 allow ipv6 mtu 1500 ipv6 nd dad makes an attempt 0 ipv6 nd ra suppress all no ipv6 redirects ipv6 ospf 1 space 0 bfd interval 1000 min_rx 1000 multiplier 5
Right here’s the response I bought:
In some unspecified time in the future, many people find yourself troubleshooting on the protocol stage (packet seize or it didn’t occur, proper?), the place issues get advanced in a short time. On this case, you’ll be able to paste the decoded output of a packet seize (equivalent to that from Wireshark or Tshark) to the Deep Community Mannequin AI Assistant, which might break down the body particulars for you. It may possibly establish hard-to-spot points and dramatically enhance the efficacy of deep networking troubleshooting.
The AI assistant may give you extra which means and context than you would possibly get with different instruments. I attempted this with a problematic SNMPv3 packet. The AI assistant regarded on the worth of the fields and defined them to me. Whereas Wireshark confirmed me the sphere names, the AI assistant defined that one area, the msgAuthoritativeEngineTime, represented the variety of seconds a tool had been on-line, which was 61411 (roughly seven weeks). The factor is, I simply booted that gadget. So my SNMP supervisor was confused, and the SNMPv3 entice wasn’t being trusted. Bug discovered!
Whereas most of us are fairly acquainted with a variety of community applied sciences, we will not be consultants in each one of many protocols we run on our community. Subsequently, take into account how helpful this may be for a protocol you’re not extremely educated about on the area stage. The AI assistant is superb at analyzing these fields and explaining their network-relevant context. Whereas the assistant gained’t resolve the issue for you, when used correctly, it may give you some good hints. When you perceive extra about these fields, making use of some reasoning and fixing the bug is far simpler.
These are simply a number of the ways in which the Deep Community Mannequin AI Assistant could possibly be useful to skilled community engineers. I hope they’re a helpful springboard in your considering. In the event you strive them out, I’d be excited to listen to in regards to the outcomes you’re getting.
However I’d be much more excited to listen to about use circumstances you’ve give you that I would by no means consider. AI is an extremely highly effective software that may make us extra environment friendly and, frankly, much less careworn. However we should determine the very best methods to make use of them, and we’re all on that journey collectively.
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